Abstract
The detection of complex spatially and temporally varying coherent structures in data from highly nonlinear and non-Gaussian systems is a challenging problem in a wide range of scientific disciplines. This is the case in the analysis of Doppler on Wheels (DOW) mobile Doppler radar (MDR) data where the goal is to detect rapidly evolving coherent storm structures that reflect the complex interplay of nonlinear dynamical processes. Estimating and quantifying such structures from the noisy and relatively sparsely sampled MDR data poses a difficult inverse problem for which traditional analysis methods such as expert and subjective pattern recognition, thresholding, and contouring choices can be difficult. In this paper the authors investigate the application of a recently developed objective method for the analysis of spatiotemporal data called the entropy field decomposition (EFD) to the problem of the analysis of MDR data in tornadic supercells. The EFD method is based on a field theoretic reformulation of Bayesian probability theory that incorporates prior information from the coupling structure within the data to automatically detect multivariate spatiotemporal modes. The method is first applied to data from a numerically simulated tornadic supercell in order to validate the method’s ability to detect and quantify known storm-scale features. It is then applied to actual MDR data collected during the evolution of a tornadic supercell—data that have been analyzed previously by experts. The authors demonstrate the ability of the EFD method to detect spatiotemporal features currently believed to be related to tornadogenesis. This new method has the potential to provide improved and objective analysis/detection with increased sensitivity to nonlinear and non-Gaussian spatially and temporally coherent features related to tornadogenesis and thus offers the potential to aid in the study, prediction, and warnings of tornadoes.
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